Transfer learning enables the adaption of models to handle mismatches of distributions across sessions or across subjects. In this paper, we proposed a new transfer learning algorithm to classify motor imagery EEG data. By analyzing the power spectrum of EEG data related to motor imagery, the shared features across sessions or across subjects, namely, the mean and variance of model parameters, are extracted. Then, select the data sets that were most relevant to the new data set according to Euclidean distance to update the shared features. Finally, utilize the shared features and subject/session-specific features jointly to generate a new model. We evaluated our algorithm by analyzing the motor imagery EEG data from 10 healthy participants and a public data set from BCI competition IV. The classification accuracy of the proposed transfer learning is higher than that of traditional machine learning algorithms. The results of the paired t test showed that the classification results of PSD and the transfer learning algorithm were significantly different (p = 2.0946e-9), and the classification results of CSP and the transfer learning algorithm were significantly different (p = 1.9122e-6). The test accuracy of data set 2a of BCI competition IV was 85.7% ± 5.4%, which was higher than that of related traditional machine learning algorithms. Preliminary results suggested that the proposed algorithm can be effectively applied to the classification of motor imagery EEG signals across sessions and across subjects and the performance is better than that of the traditional machine learning algorithms. It can be promising to be applied to the field of brain-computer interface (BCI). Graphical abstract.

中文翻译：

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通过基于运动图像的脑机接口系统中的转移学习，跨会话和跨主题进行EEG分类。

转移学习使模型可以适应跨会话或跨主题的分布不匹配问题。在本文中，我们提出了一种新的转移学习算法来对运动图像脑电数据进行分类。通过分析与运动图像有关的脑电数据的功率谱，提取了跨会话或跨主题的共享特征，即模型参数的均值和方差。然后，根据欧几里得距离选择与新数据集最相关的数据集以更新共享功能。最后，共同使用共享功能和特定于主题/会话的功能来生成新模型。我们通过分析来自10名健康参与者的运动图像EEG数据和BCI竞赛IV的公共数据集来评估我们的算法。提出的转移学习的分类精度高于传统的机器学习算法。配对t检验的结果表明PSD的分类结果和转移学习算法有显着差异（p = 2.0946e-9），CSP和转移学习算法的分类结果有显着差异（p = 1.9122e） -6）。BCI竞赛IV的数据集2a的测试准确性为85.7％±5.4％，高于相关的传统机器学习算法。初步结果表明，该算法可以有效地应用于跨会话和跨主题的运动图像脑电信号分类，其性能优于传统的机器学习算法。有望将其应用于脑机接口（BCI）领域。图形概要。